FSST: Frequency-Space Signal Transformation of Massive MIMO Channels
2021
会议录名称2021 IEEE GLOBAL COMMUNICATIONS CONFERENCE (GLOBECOM)
ISSN2334-0983
发表状态已发表
DOI10.1109/GLOBECOM46510.2021.9685595
摘要

High overhead of sharing and feedback and high computational complexity are common problems in multi-cell processing. In this paper, a novel framework for bidirectional signal transformation between space and frequency domains of massive MIMO channels is proposed to reduce system processing overhead and complexity. We design new space and frequency features and build the framework by two off-line trained neural networks (NN). Moreover, the uniqueness of spatial features is proved. Average errors of uni- and bi-directional transformation are 7.6% and 73%. When applying the framework to inter-cell interference coordination (ICIC), the system and edge throughput are both increased compared to the traditional scheme with low information sharing overhead.

关键词Bidirectional transformation framework space domain neural networks massive MIMO 3D channel model
会议名称IEEE Global Communications Conference (GLOBECOM)
出版地345 E 47TH ST, NEW YORK, NY 10017 USA
会议地点null,Madrid,SPAIN
会议日期DEC 07-11, 2021
URL查看原文
收录类别EI ; CPCI ; CPCI-S
语种英语
资助项目National Key R&D Program of China[2019YFB1803304]
WOS研究方向Computer Science ; Engineering ; Telecommunications
WOS类目Computer Science, Information Systems ; Computer Science, Theory & Methods ; Engineering, Electrical & Electronic ; Telecommunications
WOS记录号WOS:000790747203017
出版者IEEE
EISSN2576-6813
来源库IEEE
文献类型会议论文
条目标识符https://kms.shanghaitech.edu.cn/handle/2MSLDSTB/183438
专题信息科学与技术学院_硕士生
信息科学与技术学院_PI研究组_杨旸组
信息科学与技术学院_PI研究组_周勇组
创意与艺术学院_特聘教授组_汪军组
通讯作者Zhu, Lei
作者单位
1.ShanghaiTech Univ, Sch Informat Sci & Technol, Shanghai, Peoples R China
2.Chinese Acad Sci, Shanghai Inst Microsyst & Informat Technol, Shanghai, Peoples R China
3.Univ Chinese Acad Sci, Beijing, Peoples R China
第一作者单位信息科学与技术学院
通讯作者单位信息科学与技术学院
第一作者的第一单位信息科学与技术学院
推荐引用方式
GB/T 7714
Zhu, Lei,Gao, Guoliang,Li, Kai,et al. FSST: Frequency-Space Signal Transformation of Massive MIMO Channels[C]. 345 E 47TH ST, NEW YORK, NY 10017 USA:IEEE,2021.
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